Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner’s Note
Examiner has cited particular columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to
fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Golovanov et al. (US 2023/0206011) and in view of Park et al. (US 2022/0374615).
As to claim 1, Golovanov teaches a method for training a machine-trained model 299 for translating text/speech comprising: receiving original training examples 400 that include, first-language textual transcripts 410 and second-language textual transcripts 420 of translations in a second language that is different than the first language; producing converted/augmented training examples 470, 480 by correcting instances of gender bias in the second-language textual transcripts (Pars.117-118), the instances of gender bias having second-language transcripts 480 having gender forms that does not necessarily correspond to the user 102 gender; and training parameters/mechanisms of the machine-trained model (Fig.5) based on the converted training examples (Pars.80, 91, 95, 97, 99-109, 116-118, 126-129; Figs.1-7).
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It is noted that Golovanov doesn’t explicitly teach where the input texts in the source/first language are obtained from speech. However, Park teaches a translations system configured to determine context/gender information (from the text or input voice), translate the received text based on the context information, and generate context based translation, wherein the source text is obtained from voice input 310 (Figs.3, 12-13; Pars.63, 67, 71, 76, 91-92, 126-129).
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The combination of the analogous arts would be obvious to one of ordinary skill in the art before the time of applicant’s invention for the purpose of allowing the user to make a speech input.
Claim(s) 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Golovanov et al. (US 2023/0206011) in view of Park et al. (US 2022/0374615) and further in view of Salz (US 2015/0161110).
As to claim 2, even though Golovano teaches iteratively training the translation model (Pars.129), he doesn’t explicitly teach making gender assumption and producing the original translation based on the assumed gender.
However, Salz in same field teaches a translation method comprising the steps of receiving input text phrase 504 in a first language, determining/predicting the gender of the text receiver 508 and generating a first gender biased translation 512 for the input phrase based on the assumed gender and a generating modified translation 528 correcting the assumed gender and training the language model for the new phrase (Fig.5; Pars.20-23, 31, 35-39, 44).
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The modification, to predict the gender, and the combination of the analogous teachings would be obvious to one of ordinary skill in the art before the time of applicant’s invention for the purpose of expediting the training/translation process.
As to claim 3, Salz teaches wherein the determining concludes that the first-language textual transcript and/or the second-language textual transcript is capable of gender bias because the first-language textual transcript and/or the second-language textual transcript includes a first-person pronoun (Pars.37-38).
As to claim 4, Golovanov teaches wherein the determining concludes that the first-language textual transcript and/or the second-language textual transcript is capable of gender bias based on analysis performed by the machine-trained language model (Figs.5-6).
Claim(s) 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Golovanov et al. (US 2023/0206011) in view of Park et al. (US 2022/0374615) and Salz (US 2015/0161110) and further in view of Marwah et al. (US 2018/0157647).
As to claims 5-6, Golovanov doesn’t explicitly teach where the original and converted phrases are mapped as claimed. However, Marwah teaches gender aware language translation system for translating text phrase 410 in a first language to a second language 420 utilizing gender biased translation examples/phrases, where the phrases are mapped/converted to gender biased translations 452-458 based on detected gender conditions 432-436 of the user/recipient gender to generate correct translations indicative of the user gender (Pars.72-83; Figs.4-5).
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The combination of the analogous teachings would be obvious to one of ordinary skill in the art for the purpose of efficiently applying the right translation phrase according to the person’s gender thereby speeding up the process.
As to claim 6, Marwah teaches where generating gender bias translation phrases into a second language comprise gender forms including gender-male, gender-female, gender-neutral, gender-unknown, etc. (Pars.78-86).
Allowable Subject Matter
Claims 7-11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 7 is allowable, because the prior arts cited do not teach wherein the training of the parameters of the machine-trained model comprises, for a particular converted training example having an audio signal that expresses speech in the first language, a ground-truth second-language textual transcript, and a ground-truth input gender: producing feature information associated with the audio signal; converting the feature information into a model-generated second-language textual transcript using the machine-trained model; comparing the model-generated second-language textual transcript with the ground-truth second-language textual transcript, to produce a translation loss; converting the feature information associated with the audio signal into a model-generated input gender; comparing the model-generated input gender with the ground-truth input gender, to produce a gender loss; and updating the parameters of the machine-trained model based on a combination of the translation loss and gender loss.
Claim 10 is allowable, because the prior arts cited above do not teach wherein the training of the parameters of the machine-trained model comprises, for a particular converted training example having an audio signal and a gender preference signal that identifies a translation mode selected from among a set of translation modes: producing feature information based on the audio signal and the gender preference signal; and converting the feature information into a second-language textual transcript using the machine-trained model, the parameters of the machine-trained model being capable of producing translations in the different translation modes, including: a first translation mode for producing first translations in masculine form irrespective of characteristics of audio signals that are input the machine-trained model; a second translation mode for producing second translations in feminine form irrespective of characteristics of the audio signals, and a third translation mode for producing a mix of masculine and feminine forms based on the characteristics of the audio signals.
Allowable Subject Matter
Claims 12-20 are allowed.
The following is an examiner’s statement of reasons for allowance: Claims 12 and 17 are allowed for the same reason provided for claims 7 and 10 respectively.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL DEMELASH ABEBE whose telephone number is (571)272-7615. The examiner can normally be reached monday-friday 7-4.
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/DANIEL ABEBE/Primary Examiner, Art Unit 2657